Localization atomic force microscopy.

Understanding structural dynamics of biomolecules at the single-molecule level is vital to advancing our knowledge of molecular mechanisms. Currently, there are few techniques that can capture dynamics at the sub-nanometre scale and in physiologically relevant conditions. Atomic force microscopy (AFM)1 has the advantage of analysing unlabelled single molecules in physiological buffer and at ambient temperature and pressure, but its resolution limits the assessment of conformational details of biomolecules2. Here we present localization AFM (LAFM), a technique developed to overcome current resolution limitations. By applying localization image reconstruction algorithms3 to peak positions in high-speed AFM and conventional AFM data, we increase the resolution beyond the limits set by the tip radius, and resolve single amino acid residues on soft protein surfaces in native and dynamic conditions. LAFM enables the calculation of high-resolution maps from either images of many molecules or many images of a single molecule acquired over time, facilitating single-molecule structural analysis. LAFM is a post-acquisition image reconstruction method that can be applied to any biomolecular AFM dataset.

Structural basis of omega-3 fatty acid transport across the blood-brain barrier.

Docosahexaenoic acid is an omega-3 fatty acid that is essential for neurological development and function, and it is supplied to the brain and eyes predominantly from dietary sources1-6. This nutrient is transported across the blood-brain and blood-retina barriers in the form of lysophosphatidylcholine by major facilitator superfamily domain containing 2A (MFSD2A) in a Na+-dependent manner7,8. Here we present the structure of MFSD2A determined using single-particle cryo-electron microscopy, which reveals twelve transmembrane helices that are separated into two pseudosymmetric domains. The transporter is in an inward-facing conformation and features a large amphipathic cavity that contains the Na+-binding site and a bound lysolipid substrate, which we confirmed using native mass spectrometry. Together with our functional analyses and molecular dynamics simulations, this structure reveals details of how MFSD2A interacts with substrates and how Na+-dependent conformational changes allow for the release of these substrates into the membrane through a lateral gate. Our work provides insights into the molecular mechanism by which this atypical major facility superfamily transporter mediates the uptake of lysolipids into the brain, and has the potential to aid in the delivery of neurotherapeutic agents.

A cholesterol switch controls phospholipid scrambling by G protein-coupled receptors.

Class A G protein-coupled receptors (GPCRs), a superfamily of cell membrane signaling receptors, moonlight as constitutively active phospholipid scramblases. The plasma membrane of metazoan cells is replete with GPCRs yet has a strong resting trans-bilayer phospholipid asymmetry, with the signaling lipid phosphatidylserine confined to the cytoplasmic leaflet. To account for the persistence of this lipid asymmetry in the presence of GPCR scramblases, we hypothesized that GPCR-mediated lipid scrambling is regulated by cholesterol, a major constituent of the plasma membrane. We now present a technique whereby synthetic vesicles reconstituted with GPCRs can be supplemented with cholesterol to a level similar to that of the plasma membrane and show that the scramblase activity of two prototypical GPCRs, opsin and the ß1-adrenergic receptor, is impaired upon cholesterol loading. Our data suggest that cholesterol acts as a switch, inhibiting scrambling above a receptor-specific threshold concentration to disable GPCR scramblases at the plasma membrane.

Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations.

Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine learning algorithms, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)-based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.

Molecular simulations and NMR reveal how lipid fluctuations affect membrane mechanics.

Lipid bilayers form the main matrix of functional cell membranes, and their dynamics underlie a host of physical and biological processes. Here we show that elastic membrane properties and collective molecular dynamics (MD) are related by the mean-square amplitudes (order parameters) and relaxation rates (correlation times) of lipid acyl chain motions. We performed all-atom MD simulations of liquid-crystalline bilayers that allow direct comparison with carbon-hydrogen (CH) bond relaxations measured with NMR spectroscopy. Previous computational and theoretical approaches have assumed isotropic relaxation, which yields inaccurate description of lipid chain dynamics and incorrect data interpretation. Instead, the new framework includes a fixed bilayer normal (director axis) and restricted anisotropic motion of the CH bonds in accord with their segmental order parameters, enabling robust validation of lipid force fields. Simulated spectral densities of thermally excited CH bond fluctuations exhibited well-defined spin-lattice (Zeeman) relaxations analogous to those in NMR measurements. Their frequency signature could be fit to a simple power-law function, indicative of nematic-like collective dynamics. Moreover, calculated relaxation rates scaled as the squared order parameters yielding an apparent κC modulus for bilayer bending. Our results show a strong correlation with κC values obtained from solid-state NMR studies of bilayers without and with cholesterol as validated by neutron spin-echo measurements of membrane elasticity. The simulations uncover a critical role of interleaflet coupling in membrane mechanics and thus provide important insights into molecular sites of emerging elastic properties within lipid bilayers.

Phosphatidylinositol phosphates modulate interactions between the StarD4 sterol trafficking protein and lipid membranes.

There is substantial evidence for extensive nonvesicular sterol transport in cells. For example, lipid transfer by the steroidogenic acute regulator-related proteins (StarD) containing a StarT domain has been shown to involve several pathways of nonvesicular trafficking. Among the soluble StarT domain-containing proteins, StarD4 is expressed in most tissues and has been shown to be an effective sterol transfer protein. However, it was unclear whether the lipid composition of donor or acceptor membranes played a role in modulating StarD4-mediated transport. Here, we used fluorescence-based assays to demonstrate a phosphatidylinositol phosphate (PIP)-selective mechanism by which StarD4 can preferentially extract sterol from liposome membranes containing certain PIPs (especially, PI(4,5)P2 and to a lesser degree PI(3,5)P2). Monophosphorylated PIPs and other anionic lipids had a smaller effect on sterol transport. This enhancement of transport was less effective when the same PIPs were present in the acceptor membranes. Furthermore, using molecular dynamics (MD) simulations, we mapped the key interaction sites of StarD4 with PIP-containing membranes and identified residues that are important for this interaction and for accelerated sterol transport activity. We show that StarD4 recognizes membrane-specific PIPs through specific interaction with the geometry of the PIP headgroup as well as the surrounding membrane environment. Finally, we also observed that StarD4 can deform membranes upon longer incubations. Taken together, these results suggest a mechanism by which PIPs modulate cholesterol transfer activity via StarD4.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

How cholesterol stiffens unsaturated lipid membranes.

Cholesterol is an integral component of eukaryotic cell membranes and a key molecule in controlling membrane fluidity, organization, and other physicochemical parameters. It also plays a regulatory function in antibiotic drug resistance and the immune response of cells against viruses, by stabilizing the membrane against structural damage. While it is well understood that, structurally, cholesterol exhibits a densification effect on fluid lipid membranes, its effects on membrane bending rigidity are assumed to be nonuniversal; i.e., cholesterol stiffens saturated lipid membranes, but has no stiffening effect on membranes populated by unsaturated lipids, such as 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC). This observation presents a clear challenge to structure-property relationships and to our understanding of cholesterol-mediated biological functions. Here, using a comprehensive approach-combining neutron spin-echo (NSE) spectroscopy, solid-state deuterium NMR (2H NMR) spectroscopy, and molecular dynamics (MD) simulations-we report that cholesterol locally increases the bending rigidity of DOPC membranes, similar to saturated membranes, by increasing the bilayer's packing density. All three techniques, inherently sensitive to mesoscale bending fluctuations, show up to a threefold increase in effective bending rigidity with increasing cholesterol content approaching a mole fraction of 50%. Our observations are in good agreement with the known effects of cholesterol on the area-compressibility modulus and membrane structure, reaffirming membrane structure-property relationships. The current findings point to a scale-dependent manifestation of membrane properties, highlighting the need to reassess cholesterol's role in controlling membrane bending rigidity over mesoscopic length and time scales of important biological functions, such as viral budding and lipid-protein interactions.

Conformational transitions in BTG1 antiproliferative protein and their modulation by disease mutants.

B cell translocation gene 1 (BTG1) protein belongs to the BTG/transducer of ERBB2 (TOB) family of antiproliferative proteins whose members regulate various key cellular processes such as cell cycle progression, apoptosis, and differentiation. Somatic missense mutations in BTG1 are found in ∼70% of a particularly malignant and disseminated subtype of diffuse large B cell lymphoma (DLBCL). Antiproliferative activity of BTG1 has been linked to its ability to associate with transcriptional cofactors and various enzymes. However, molecular mechanisms underlying these functional interactions and how the disease-linked mutations in BTG1 affect these mechanisms are currently unknown. To start filling these knowledge gaps, here, using atomistic molecular dynamics (MD) simulations, we explored structural, dynamic, and kinetic characteristics of BTG1 protein, and studied how various DLBCL mutations affect these characteristics. We focused on the protein region formed by α2 and α4 helices, as this interface has been reported not only to serve as a binding hotspot for several cellular partners but also to harbor sites for the majority of known DLBCL mutations. Markov state modeling analysis of extensive MD simulations revealed that the α2-α4 interface in the wild-type (WT) BTG1 undergoes conformational transitions between closed and open metastable states. Importantly, we show that some of the mutations in this region that are observed in DLBCL, such as Q36H, F40C, Q45P, E50K (in α2), and A83T and A84E (in α4), either overstabilize one of these two metastable states or give rise to new conformations in which these helices are distorted (i.e., kinked or unfolded). Based on these results, we conclude that the rapid interconversion between the closed and open conformations of the α2-α4 interface is an essential component of the BTG1 functional dynamics that can prime the protein for functional associations with its binding partners. Disruption of the native dynamic equilibrium by DLBCL mutants leads to the ensemble of conformations in BTG1 that are unlikely structurally and/or kinetically to enable productive functional interactions with the binding proteins.

The allosteric mechanism of substrate-specific transport in SLC6 is mediated by a volumetric sensor.

Neurotransmitter:sodium symporters (NSSs) in the SLC6 family terminate neurotransmission by coupling the thermodynamically favorable transport of ions to the thermodynamically unfavorable transport of neurotransmitter back into presynaptic neurons. Results from many structural, functional, and computational studies on LeuT, a bacterial NSS homolog, have provided critical insight into the mechanism of sodium-coupled transport, but the mechanism underlying substrate-specific transport rates is still not understood. We present a combination of molecular dynamics simulations, single-molecule fluorescence resonance energy transfer (smFRET) imaging, and measurements of Na+ binding and substrate transport that reveals an allosteric substrate specificity mechanism. In this mechanism, residues F259 and I359 in the substrate binding pocket couple the binding of substrate to Na+ release from the Na2 site by allosterically modulating the stability of a partially open, inward-facing state. We propose a model for transport selectivity in which residues F259 and I359 act as a volumetric sensor that inhibits the transport of bulky amino acids.

Ca2+-dependent mechanism of membrane insertion and destabilization by the SARS-CoV-2 fusion peptide.

Cell penetration after recognition of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus by the ACE2 receptor and the fusion of its viral envelope membrane with cellular membranes are the early steps of infectivity. A region of the Spike protein of the virus, identified as the "fusion peptide" (FP), is liberated at its N-terminal site by a specific cleavage occurring in concert with the interaction of the receptor-binding domain of the Spike. Studies have shown that penetration is enhanced by the required binding of Ca2+ ions to the FPs of coronaviruses, but the mechanisms of membrane insertion and destabilization remain unclear. We have predicted the preferred positions of Ca2+ binding to the SARS-CoV-2-FP, the role of Ca2+ ions in mediating peptide-membrane interactions, the preferred mode of insertion of the Ca2+-bound SARS-CoV-2-FP, and consequent effects on the lipid bilayer from extensive atomistic molecular dynamics simulations and trajectory analyses. In a systematic sampling of the interactions of the Ca2+-bound peptide models with lipid membranes, SARS-CoV-2-FP penetrated the bilayer and disrupted its organization only in two modes involving different structural domains. In one, the hydrophobic residues F833/I834 from the middle region of the peptide are inserted. In the other, more prevalent mode, the penetration involves residues L822/F823 from the LLF motif, which is conserved in CoV-2-like viruses, and is achieved by the binding of Ca2+ ions to the D830/D839 and E819/D820 residue pairs. FP penetration is shown to modify the molecular organization in specific areas of the bilayer, and the extent of membrane binding of the SARS-CoV-2 FP is significantly reduced in the absence of Ca2+ ions. These findings provide novel mechanistic insights regarding the role of Ca2+ in mediating SARS-CoV-2 fusion and provide a detailed structural platform to aid the ongoing efforts in rational design of compounds to inhibit SARS-CoV-2 cell entry.

Transport domain unlocking sets the uptake rate of an aspartate transporter.

Glutamate transporters terminate neurotransmission by clearing synaptically released glutamate from the extracellular space, allowing repeated rounds of signalling and preventing glutamate-mediated excitotoxicity. Crystallographic studies of a glutamate transporter homologue from the archaeon Pyrococcus horikoshii, GltPh, showed that distinct transport domains translocate substrates into the cytoplasm by moving across the membrane within a central trimerization scaffold. Here we report direct observations of these 'elevator-like' transport domain motions in the context of reconstituted proteoliposomes and physiological ion gradients using single-molecule fluorescence resonance energy transfer (smFRET) imaging. We show that GltPh bearing two mutations introduced to impart characteristics of the human transporter exhibits markedly increased transport domain dynamics, which parallels an increased rate of substrate transport, thereby establishing a direct temporal relationship between transport domain motion and substrate uptake. Crystallographic and computational investigations corroborated these findings by revealing that the 'humanizing' mutations favour structurally 'unlocked' intermediate states in the transport cycle exhibiting increased solvent occupancy at the interface between the transport domain and the trimeric scaffold.

Membrane lipids are both the substrates and a mechanistically responsive environment of TMEM16 scramblase proteins.

Recent discoveries about functional mechanisms of proteins in the TMEM16 family of phospholipid scramblases have illuminated the dual role of the membrane as both the substrate and a mechanistically responsive environment in the wide range of physiological processes and genetic disorders in which they are implicated. This is highlighted in the review of recent findings from our collaborative investigations of molecular mechanisms of TMEM16 scramblases that emerged from iterative functional, structural, and computational experimentation. In the context of this review, we present new MD simulations and trajectory analyses motivated by the fact that new structural information about the TMEM16 scramblases is emerging from cryo-EM determinations in lipid nanodiscs. Because the functional environment of these proteins in in vivo and in in vitro is closer to flat membranes, we studied comparatively the responses of the membrane to the TMEM16 proteins in flat membranes and nanodiscs. We find that bilayer shapes in the nanodiscs are very different from those observed in the flat membrane systems, but the function-related slanting of the membrane observed at the nhTMEM16 boundary with the protein is similar in the nanodiscs and in the flat bilayers. This changes, however, in the bilayer composed of longer-tail lipids, which is thicker near the phospholipid translocation pathway, which may reflect an enhanced tendency of the long tails to penetrate the pathway and create, as shown previously, a nonconductive environment. These findings support the correspondence between the mechanistic involvement of the lipid environment in the flat membranes, and the nanodiscs. © 2019 Wiley Periodicals, Inc.

A New Computational Method for Membrane Compressibility: Bilayer Mechanical Thickness Revisited.

Because lipid bilayers can bend and stretch in ways similar to thin elastic sheets, physical models of bilayer deformation have utilized mechanical constants such as the moduli for bending rigidity (κC) and area compressibility (KA). However, the use of these models to quantify the energetics of membrane deformation associated with protein-membrane interactions, and the membrane response to stress is often hampered by the shortage of experimental data suitable for the estimation of the mechanical constants of various lipid mixtures. Although computational tools such as molecular dynamics simulations can provide alternative means to estimate KA values, current approaches suffer significant technical limitations. Here, we present a novel, to our knowledge, computational framework that allows for a direct estimation of KA values for individual bilayer leaflets. The theory is based on the concept of elasticity and derives KA from real-space analysis of local thickness fluctuations sampled in molecular dynamics simulations. We explore and validate the model on a large set of single and multicomponent bilayers of different lipid compositions and sizes, simulated at different temperatures. The calculated bilayer compressibility moduli agree with values estimated previously from experiments and those obtained from a standard computational method based on a series of constrained tension simulations. We further validate our framework in a comparison with an existing polymer brush model and confirm the polymer brush model's predicted linear relationship with proportionality coefficient of 24, using elastic parameters calculated from the simulation trajectories. The robustness of the results that emerge from the method allows us to revisit the origins of the bilayer mechanical (compressible) thickness and in particular its dependence on acyl-chain unsaturation and the presence of cholesterol.

How structural elements evolving from bacterial to human SLC6 transporters enabled new functional properties.

BACKGROUND: Much of the structure-based mechanistic understandings of the function of SLC6A neurotransmitter transporters emerged from the study of their bacterial LeuT-fold homologs. It has become evident, however, that structural differences such as the long N- and C-termini of the eukaryotic neurotransmitter transporters are involved in an expanded set of functional properties to the eukaryotic transporters. These functional properties are not shared by the bacterial homologs, which lack the structural elements that appeared later in evolution. However, mechanistic insights into some of the measured functional properties of the eukaryotic transporters that have been suggested to involve these structural elements are sparse or merely descriptive. RESULTS: To learn how the structural elements added in evolution enable mechanisms of the eukaryotic transporters in ways not shared with their bacterial LeuT-like homologs, we focused on the human dopamine transporter (hDAT) as a prototype. We present the results of a study employing large-scale molecular dynamics simulations and comparative Markov state model analysis of experimentally determined properties of the wild-type and mutant hDAT constructs. These offer a quantitative outline of mechanisms in which a rich spectrum of interactions of the hDAT N-terminus and C-terminus contribute to the regulation of transporter function (e.g., by phosphorylation) and/or to entirely new phenotypes (e.g., reverse uptake (efflux)) that were added in evolution. CONCLUSIONS: The findings are consistent with the proposal that the size of eukaryotic neurotransmitter transporter termini increased during evolution to enable more functions (e.g., efflux) not shared with the bacterial homologs. The mechanistic explanations for the experimental findings about the modulation of function in DAT, the serotonin transporter, and other eukaryotic transporters reveal separate roles for the distal and proximal segments of the much larger N-terminus in eukaryotic transporters compared to the bacterial ones. The involvement of the proximal and distal segments - such as the role of the proximal segment in sustaining transport in phosphatidylinositol 4,5-bisphosphate-depleted membranes and of the distal segment in modulating efflux - may represent an evolutionary adaptation required for the function of eukaryotic transporters expressed in various cell types of the same organism that differ in the lipid composition and protein complement of their membrane environment.

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.

Thermodynamic Coupling Function Analysis of Allosteric Mechanisms in the Human Dopamine Transporter.

Allostery plays a crucial role in the mechanism of neurotransmitter-sodium symporters, such as the human dopamine transporter. To investigate the molecular mechanism that couples the transport-associated inward release of the Na+ ion from the Na2 site to intracellular gating, we applied a combination of the thermodynamic coupling function (TCF) formalism and Markov state model analysis to a 50-µs data set of molecular dynamics trajectories of the human dopamine transporter, in which multiple spontaneous Na+ release events were observed. Our TCF approach reveals a complex landscape of thermodynamic coupling between Na+ release and inward-opening, and identifies diverse, yet well-defined roles for different Na+-coordinating residues. In particular, we identify a prominent role in the allosteric coupling for the Na+-coordinating residue D421, where mutation has previously been associated with neurological disorders. Our results highlight the power of the TCF analysis to elucidate the molecular mechanism of complex allosteric processes in large biomolecular systems.